Overview

Dataset statistics

Number of variables10
Number of observations917553
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.5 MiB
Average record size in memory36.0 B

Variable types

Numeric10

Warnings

bid_price1 is highly correlated with ask_price1 and 2 other fieldsHigh correlation
ask_price1 is highly correlated with bid_price1 and 2 other fieldsHigh correlation
bid_price2 is highly correlated with bid_price1 and 2 other fieldsHigh correlation
ask_price2 is highly correlated with bid_price1 and 2 other fieldsHigh correlation
bid_price1 is highly correlated with ask_price1 and 2 other fieldsHigh correlation
ask_price1 is highly correlated with bid_price1 and 2 other fieldsHigh correlation
bid_price2 is highly correlated with bid_price1 and 2 other fieldsHigh correlation
ask_price2 is highly correlated with bid_price1 and 2 other fieldsHigh correlation
bid_price1 is highly correlated with ask_price1 and 2 other fieldsHigh correlation
ask_price1 is highly correlated with bid_price1 and 2 other fieldsHigh correlation
bid_price2 is highly correlated with bid_price1 and 2 other fieldsHigh correlation
ask_price2 is highly correlated with bid_price1 and 2 other fieldsHigh correlation
bid_price2 is highly correlated with ask_price1 and 2 other fieldsHigh correlation
ask_price1 is highly correlated with bid_price2 and 2 other fieldsHigh correlation
bid_price1 is highly correlated with bid_price2 and 2 other fieldsHigh correlation
ask_price2 is highly correlated with bid_price2 and 2 other fieldsHigh correlation

Reproduction

Analysis started2021-07-30 04:54:54.802502
Analysis finished2021-07-30 04:58:52.661237
Duration3 minutes and 57.86 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

time_id
Real number (ℝ≥0)

Distinct3830
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15980.05691
Minimum5
Maximum32767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-07-30T11:58:52.910215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile1551
Q17759
median15772
Q323834
95-th percentile31071
Maximum32767
Range32762
Interquartile range (IQR)16075

Descriptive statistics

Standard deviation9381.778917
Coefficient of variation (CV)0.5870929604
Kurtosis-1.164642838
Mean15980.05691
Median Absolute Deviation (MAD)8027
Skewness0.05211303561
Sum1.466254916 × 1010
Variance88017775.64
MonotonicityIncreasing
2021-07-30T11:58:53.257558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14243549
 
0.1%
32342538
 
0.1%
26874523
 
0.1%
4004505
 
0.1%
4560505
 
0.1%
24600502
 
0.1%
25010499
 
0.1%
13948498
 
0.1%
30974495
 
0.1%
9343487
 
0.1%
Other values (3820)912452
99.4%
ValueCountFrequency (%)
5302
< 0.1%
11200
< 0.1%
16188
< 0.1%
31120
 
< 0.1%
62176
< 0.1%
72263
< 0.1%
97368
< 0.1%
103294
< 0.1%
109236
< 0.1%
123436
< 0.1%
ValueCountFrequency (%)
32767228
< 0.1%
32763307
< 0.1%
32758188
< 0.1%
32753206
< 0.1%
32751297
< 0.1%
32750149
< 0.1%
32748170
< 0.1%
32746209
< 0.1%
32739228
< 0.1%
32736212
< 0.1%

seconds_in_bucket
Real number (ℝ≥0)

Distinct600
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293.6920145
Minimum0
Maximum599
Zeros3830
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2021-07-30T11:58:53.616025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26
Q1142
median292
Q3444
95-th percentile567
Maximum599
Range599
Interquartile range (IQR)302

Descriptive statistics

Standard deviation173.5964405
Coefficient of variation (CV)0.5910832841
Kurtosis-1.206721482
Mean293.6920145
Median Absolute Deviation (MAD)151
Skewness0.02714501101
Sum269477989
Variance30135.72414
MonotonicityNot monotonic
2021-07-30T11:58:53.982546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03830
 
0.4%
11975
 
0.2%
31734
 
0.2%
21716
 
0.2%
51660
 
0.2%
951658
 
0.2%
901656
 
0.2%
621646
 
0.2%
651642
 
0.2%
1011640
 
0.2%
Other values (590)898396
97.9%
ValueCountFrequency (%)
03830
0.4%
11975
0.2%
21716
0.2%
31734
0.2%
41634
0.2%
51660
0.2%
61631
0.2%
71629
0.2%
81576
0.2%
91564
0.2%
ValueCountFrequency (%)
599673
0.1%
598889
0.1%
5971082
0.1%
5961177
0.1%
5951250
0.1%
5941330
0.1%
5931375
0.1%
5921339
0.1%
5911385
0.2%
5901445
0.2%

bid_price1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct81771
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9994952679
Minimum0.9382413626
Maximum1.045641184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2021-07-30T11:58:54.360599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.9382413626
5-th percentile0.9948065281
Q10.9983679056
median0.9996315837
Q31.000753284
95-th percentile1.003736973
Maximum1.045641184
Range0.1073998213
Interquartile range (IQR)0.002385377884

Descriptive statistics

Standard deviation0.003646981902
Coefficient of variation (CV)0.003648823593
Kurtosis37.35269165
Mean0.9994952679
Median Absolute Deviation (MAD)0.001185595989
Skewness-1.418036938
Sum917089.875
Variance1.33004778 × 10-5
MonotonicityNot monotonic
2021-07-30T11:58:54.752316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15553
 
0.6%
1.000024199340
 
< 0.1%
1.000024319332
 
< 0.1%
1.000023365328
 
< 0.1%
1.000023484320
 
< 0.1%
1.00002408283
 
< 0.1%
0.9999758601273
 
< 0.1%
1.000023723244
 
< 0.1%
1.000023842244
 
< 0.1%
1.000024438229
 
< 0.1%
Other values (81761)909407
99.1%
ValueCountFrequency (%)
0.93824136264
< 0.1%
0.93830108644
< 0.1%
0.93836086993
< 0.1%
0.93859988452
 
< 0.1%
0.9386596681
 
< 0.1%
0.93871939183
< 0.1%
0.93883889916
< 0.1%
0.93889868265
< 0.1%
0.93895846611
 
< 0.1%
0.93907797341
 
< 0.1%
ValueCountFrequency (%)
1.0456411845
 
< 0.1%
1.042478813
 
< 0.1%
1.04223549416
< 0.1%
1.0421746973
 
< 0.1%
1.04146647535
< 0.1%
1.0414075852
 
< 0.1%
1.0413485773
 
< 0.1%
1.0404111155
 
< 0.1%
1.0402861833
 
< 0.1%
1.04010701217
< 0.1%

ask_price1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct77466
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.000525832
Minimum0.9443365335
Maximum1.056891799
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2021-07-30T11:58:55.139416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.9443365335
5-th percentile0.9963750243
Q10.999222517
median1.000331044
Q31.001560211
95-th percentile1.00535655
Maximum1.056891799
Range0.1125552654
Interquartile range (IQR)0.002337694168

Descriptive statistics

Standard deviation0.003677648259
Coefficient of variation (CV)0.003675715532
Kurtosis34.64378357
Mean1.000525832
Median Absolute Deviation (MAD)0.001169800758
Skewness0.8512346148
Sum918035.5
Variance1.352509662 × 10-5
MonotonicityNot monotonic
2021-07-30T11:58:55.514662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15878
 
0.6%
1.000023842472
 
0.1%
1.000024319350
 
< 0.1%
1.000024438310
 
< 0.1%
1.000025511298
 
< 0.1%
1.000023603292
 
< 0.1%
1.000024199280
 
< 0.1%
1.00002408280
 
< 0.1%
1.000024676262
 
< 0.1%
1.00007081258
 
< 0.1%
Other values (77456)908873
99.1%
ValueCountFrequency (%)
0.944336533520
< 0.1%
0.944456040926
< 0.1%
0.94810730221
 
< 0.1%
0.94959515332
 
< 0.1%
0.95031416425
 
< 0.1%
0.95331799983
 
< 0.1%
0.95365858082
 
< 0.1%
0.9546053414
 
< 0.1%
0.95466667413
 
< 0.1%
0.95473420623
 
< 0.1%
ValueCountFrequency (%)
1.0568917991
 
< 0.1%
1.0566484931
 
< 0.1%
1.0550823211
 
< 0.1%
1.0542159083
 
< 0.1%
1.0541551117
< 0.1%
1.05409431511
< 0.1%
1.0540803671
 
< 0.1%
1.0538445714
 
< 0.1%
1.0531961925
< 0.1%
1.0528424981
 
< 0.1%

bid_price2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct83452
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9992983937
Minimum0.9372130632
Maximum1.043755889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2021-07-30T11:58:55.925346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.9372130632
5-th percentile0.9945313931
Q10.9981838465
median0.9994723797
Q31.000586748
95-th percentile1.003514051
Maximum1.043755889
Range0.1065428257
Interquartile range (IQR)0.002402901649

Descriptive statistics

Standard deviation0.003660179209
Coefficient of variation (CV)0.003662748961
Kurtosis37.37913132
Mean0.9992983937
Median Absolute Deviation (MAD)0.00118792057
Skewness-1.628376126
Sum916909.25
Variance1.339691244 × 10-5
MonotonicityNot monotonic
2021-07-30T11:58:56.313907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14717
 
0.5%
1.000023365366
 
< 0.1%
1.000024199344
 
< 0.1%
1.00002408280
 
< 0.1%
0.9999765754270
 
< 0.1%
0.9999758005262
 
< 0.1%
1.000023842258
 
< 0.1%
1.000023603244
 
< 0.1%
1.000024438238
 
< 0.1%
1.000024676235
 
< 0.1%
Other values (83442)910339
99.2%
ValueCountFrequency (%)
0.93721306328
< 0.1%
0.938181579110
< 0.1%
0.93824136264
 
< 0.1%
0.93830108644
 
< 0.1%
0.93854016074
 
< 0.1%
0.93859988451
 
< 0.1%
0.9386596681
 
< 0.1%
0.938779175310
< 0.1%
0.93883889911
 
< 0.1%
0.93889868261
 
< 0.1%
ValueCountFrequency (%)
1.0437558891
 
< 0.1%
1.0422354943
 
< 0.1%
1.0421746972
 
< 0.1%
1.0419923075
 
< 0.1%
1.0419315110
< 0.1%
1.041566612
 
< 0.1%
1.0414075852
 
< 0.1%
1.0413485772
 
< 0.1%
1.0412896873
 
< 0.1%
1.04070019716
< 0.1%

ask_price2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct77196
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.000727057
Minimum0.9444560409
Maximum1.057675838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2021-07-30T11:58:56.839801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.9444560409
5-th percentile0.9966229796
Q10.9993902445
median1.000495672
Q31.001744509
95-th percentile1.005660415
Maximum1.057675838
Range0.1132197976
Interquartile range (IQR)0.002354264259

Descriptive statistics

Standard deviation0.003704133211
Coefficient of variation (CV)0.003701442154
Kurtosis34.75125885
Mean1.000727057
Median Absolute Deviation (MAD)0.001172184944
Skewness1.094791055
Sum918220.125
Variance1.372060251 × 10-5
MonotonicityNot monotonic
2021-07-30T11:58:57.524019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15480
 
0.6%
1.000024438367
 
< 0.1%
1.000024319353
 
< 0.1%
1.000024199347
 
< 0.1%
1.00002408340
 
< 0.1%
1.000023723335
 
< 0.1%
1.000023842316
 
< 0.1%
1.000023603273
 
< 0.1%
1.000024676266
 
< 0.1%
0.9999758601235
 
< 0.1%
Other values (77186)909241
99.1%
ValueCountFrequency (%)
0.94445604097
 
< 0.1%
0.944515824337
< 0.1%
0.94810122252
 
< 0.1%
0.95031416421
 
< 0.1%
0.95365858082
 
< 0.1%
0.9546053411
 
< 0.1%
0.95466667414
 
< 0.1%
0.95473420622
 
< 0.1%
0.95479398972
 
< 0.1%
0.95485055451
 
< 0.1%
ValueCountFrequency (%)
1.0576758381
 
< 0.1%
1.0573782921
 
< 0.1%
1.0571349861
 
< 0.1%
1.0568917992
 
< 0.1%
1.0566484933
 
< 0.1%
1.0564661033
 
< 0.1%
1.0550823215
< 0.1%
1.0543984175
< 0.1%
1.05415511111
< 0.1%
1.0540803673
 
< 0.1%

bid_size1
Real number (ℝ≥0)

Distinct936
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.7171019
Minimum1
Maximum3221
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2021-07-30T11:58:58.314897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q122
median100
Q3157
95-th percentile311
Maximum3221
Range3220
Interquartile range (IQR)135

Descriptive statistics

Standard deviation108.6572086
Coefficient of variation (CV)0.9555045531
Kurtosis10.31237492
Mean113.7171019
Median Absolute Deviation (MAD)75
Skewness1.940216438
Sum104341468
Variance11806.38899
MonotonicityNot monotonic
2021-07-30T11:58:58.968422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100162623
 
17.7%
9089268
 
9.7%
175920
 
8.3%
20053375
 
5.8%
230532
 
3.3%
10119282
 
2.1%
30017231
 
1.9%
316938
 
1.8%
513226
 
1.4%
1013101
 
1.4%
Other values (926)426057
46.4%
ValueCountFrequency (%)
175920
8.3%
230532
3.3%
316938
 
1.8%
411511
 
1.3%
513226
 
1.4%
68314
 
0.9%
75955
 
0.6%
85174
 
0.6%
94803
 
0.5%
1013101
 
1.4%
ValueCountFrequency (%)
32211
 
< 0.1%
31201
 
< 0.1%
31001
 
< 0.1%
30254
< 0.1%
29251
 
< 0.1%
18721
 
< 0.1%
17001
 
< 0.1%
16393
< 0.1%
15503
< 0.1%
14932
< 0.1%

ask_size1
Real number (ℝ≥0)

Distinct988
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.8253496
Minimum1
Maximum16608
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2021-07-30T11:58:59.690518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q114
median93
Q3117
95-th percentile300
Maximum16608
Range16607
Interquartile range (IQR)103

Descriptive statistics

Standard deviation109.0638918
Coefficient of variation (CV)1.08171102
Kurtosis2307.499362
Mean100.8253496
Median Absolute Deviation (MAD)69
Skewness17.38395856
Sum92512602
Variance11894.93249
MonotonicityNot monotonic
2021-07-30T11:58:59.997723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100144179
 
15.7%
188157
 
9.6%
9085817
 
9.4%
20038771
 
4.2%
236157
 
3.9%
320100
 
2.2%
10118642
 
2.0%
516642
 
1.8%
414111
 
1.5%
2013690
 
1.5%
Other values (978)441287
48.1%
ValueCountFrequency (%)
188157
9.6%
236157
3.9%
320100
 
2.2%
414111
 
1.5%
516642
 
1.8%
69590
 
1.0%
76827
 
0.7%
86078
 
0.7%
95598
 
0.6%
1013119
 
1.4%
ValueCountFrequency (%)
166084
< 0.1%
47512
< 0.1%
25011
 
< 0.1%
24331
 
< 0.1%
24041
 
< 0.1%
24033
< 0.1%
24002
< 0.1%
23004
< 0.1%
22001
 
< 0.1%
21031
 
< 0.1%

bid_size2
Real number (ℝ≥0)

Distinct809
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.77024107
Minimum1
Maximum4391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2021-07-30T11:59:00.653205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q118
median100
Q3102
95-th percentile242
Maximum4391
Range4390
Interquartile range (IQR)84

Descriptive statistics

Standard deviation90.60258847
Coefficient of variation (CV)1.04416661
Kurtosis46.72589166
Mean86.77024107
Median Absolute Deviation (MAD)74
Skewness3.231694537
Sum79616295
Variance8208.829038
MonotonicityNot monotonic
2021-07-30T11:59:00.990626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100213553
23.3%
184561
 
9.2%
20053535
 
5.8%
233934
 
3.7%
2523932
 
2.6%
2622793
 
2.5%
2021039
 
2.3%
10119777
 
2.2%
517099
 
1.9%
2416590
 
1.8%
Other values (799)410740
44.8%
ValueCountFrequency (%)
184561
9.2%
233934
3.7%
316370
 
1.8%
412848
 
1.4%
517099
 
1.9%
68667
 
0.9%
76068
 
0.7%
85901
 
0.6%
95043
 
0.5%
1013797
 
1.5%
ValueCountFrequency (%)
43912
 
< 0.1%
32201
 
< 0.1%
31206
< 0.1%
29012
 
< 0.1%
28781
 
< 0.1%
28771
 
< 0.1%
28001
 
< 0.1%
25351
 
< 0.1%
25004
< 0.1%
21027
< 0.1%

ask_size2
Real number (ℝ≥0)

Distinct890
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.20306729
Minimum1
Maximum16608
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 MiB
2021-07-30T11:59:01.338057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q114
median90
Q3102
95-th percentile226
Maximum16608
Range16607
Interquartile range (IQR)88

Descriptive statistics

Standard deviation94.96838783
Coefficient of variation (CV)1.14140489
Kurtosis1081.710946
Mean83.20306729
Median Absolute Deviation (MAD)64
Skewness10.28063018
Sum76343224
Variance9018.994686
MonotonicityNot monotonic
2021-07-30T11:59:01.703015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100196573
21.4%
185059
 
9.3%
20044849
 
4.9%
235528
 
3.9%
2524997
 
2.7%
2621942
 
2.4%
2019830
 
2.2%
519230
 
2.1%
10119224
 
2.1%
318317
 
2.0%
Other values (880)432004
47.1%
ValueCountFrequency (%)
185059
9.3%
235528
3.9%
318317
 
2.0%
415424
 
1.7%
519230
 
2.1%
610207
 
1.1%
77038
 
0.8%
86227
 
0.7%
95787
 
0.6%
1013763
 
1.5%
ValueCountFrequency (%)
166081
 
< 0.1%
49004
 
< 0.1%
47511
 
< 0.1%
250021
< 0.1%
23002
 
< 0.1%
22005
 
< 0.1%
21331
 
< 0.1%
211010
< 0.1%
210024
< 0.1%
20331
 
< 0.1%

Interactions

2021-07-30T11:57:00.859319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:03.284537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:05.407654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:06.576232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:08.012431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:09.605399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:11.707009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:12.600285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:13.446064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:14.298085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:17.425891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:18.991186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:21.363079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:23.028624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:23.962583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:25.669658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:27.297973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:28.662040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:29.511735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:30.362814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:31.083458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:31.956617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:33.129524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:34.139759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:34.891398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:35.580426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:36.693091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:38.311604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:39.416309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:40.214149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:41.160132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:41.889575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:42.885502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:45.338605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:46.597828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:48.102247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:49.095869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:49.958791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:50.819338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:51.987423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:53.047621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:54.119461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:54.965296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:56.427453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:57.322701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:58.059407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:58.957956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:57:59.774707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:00.571658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:01.301077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:02.054227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:02.772952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:03.481415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:04.188293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:04.876223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:05.552866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:06.314625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:07.061510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:07.937034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:09.057107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:10.025901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:11.154401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:12.173822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:13.025012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:13.861149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:14.698862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:15.432101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:16.124788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:16.796601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:17.427969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:18.109595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:18.794144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:19.474275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:20.165337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:21.063608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:21.859921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:22.649828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:23.615066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:24.323572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:25.169435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:26.773394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:27.634184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:28.412778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:29.129064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:30.157581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:30.954075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:31.671252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:32.389644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:33.388428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:34.459308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:35.698725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:36.823175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:37.885709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:38.771785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:39.526783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:40.267602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:41.058412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:41.951693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:42.647288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-07-30T11:58:43.341893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-07-30T11:59:02.050003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-30T11:59:02.511738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-30T11:59:02.981255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-30T11:59:03.440173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-07-30T11:58:44.024723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-30T11:58:47.089661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

time_idseconds_in_bucketbid_price1ask_price1bid_price2ask_price2bid_size1ask_size1bid_size2ask_size2
0501.0014221.0023011.001371.00235332262100
1511.0014221.0023011.001371.00235331002100
2551.0014221.0023011.001371.00240531002100
3561.0014221.0023011.001371.00240531262100
4571.0014221.0023011.001371.00240531262100
55111.0014221.0023011.001371.00240531002100
65121.0014221.0023011.001371.00240531262100
75141.0014221.0023011.001371.00240531262100
85151.0014221.0023011.001371.00240531262100
95161.0014221.0023011.001371.00240531262100

Last rows

time_idseconds_in_bucketbid_price1ask_price1bid_price2ask_price2bid_size1ask_size1bid_size2ask_size2
917543327675590.9986110.9989460.9985150.99904219028200100
917544327675640.9986110.9989460.9985150.9989941902820028
917545327675650.9986110.9989460.9985150.9989941902820028
917546327675660.9986110.9989460.9985150.99904219028200100
917547327675670.9977960.9987540.9977480.9989464811310028
917548327675680.9982750.9987540.9977960.99894690904828
917549327675690.9982750.9987540.9978920.998946919020028
917550327675710.9982750.9987540.9978920.998946919010028
917551327675720.9982750.9987540.9978920.998946929010028
917552327675820.9982750.9987540.9981790.99894692902628